


import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()



def UpperCamelCase_				(				__a			,				__a			,				__a			,				__a			,				__a			,				__a = None			,				)	->   str:
      a__	:  int     =   {}

      if train_file is not None:
            a__	:  int     =   [train_file]
      if eval_file is not None:
            a__	:  Union[str, Any]     =   [eval_file]
      if test_file is not None:
            a__	:  str     =   [test_file]

      a__	:  Optional[Any]     =   datasets.load_dataset("csv"			,				data_files=__a      )
      a__	:  List[Any]     =   list(ds[list(files.keys()      )[0]].features.keys()      )
      a__	:  str     =   features_name.pop(__a      )
      a__	:  Dict     =   list(set(ds[list(files.keys()      )[0]][label_name]      )      )
      a__	:  str     =   {label: i for i, label in enumerate(__a      )}
      a__	:  Tuple     =   tokenizer.model_input_names
      a__	:  List[str]     =   {}

      if len(__a      ) == 1:
            for k in files.keys():
                  a__	:  Optional[Any]     =   ds[k].map(
                      lambda __a      : tokenizer.batch_encode_plus(
                          example[features_name[0]]			,				truncation=__a			,				max_length=__a			,				padding="max_length"      )			,				batched=__a			,				)
      elif len(__a      ) == 2:
            for k in files.keys():
                  a__	:  Dict     =   ds[k].map(
                      lambda __a      : tokenizer.batch_encode_plus(
                          (example[features_name[0]], example[features_name[1]])			,				truncation=__a			,				max_length=__a			,				padding="max_length"			,				)			,				batched=__a			,				)

      def gen_train():
            for ex in transformed_ds[datasets.Split.TRAIN]:
                  a__	:  str     =   {k: v for k, v in ex.items() if k in input_names}
                  a__	:  str     =   labelaid[ex[label_name]]
                  yield (d, label)

      def gen_val():
            for ex in transformed_ds[datasets.Split.VALIDATION]:
                  a__	:  Tuple     =   {k: v for k, v in ex.items() if k in input_names}
                  a__	:  List[Any]     =   labelaid[ex[label_name]]
                  yield (d, label)

      def gen_test():
            for ex in transformed_ds[datasets.Split.TEST]:
                  a__	:  List[Any]     =   {k: v for k, v in ex.items() if k in input_names}
                  a__	:  Optional[int]     =   labelaid[ex[label_name]]
                  yield (d, label)

      a__	:  Optional[Any]     =   (
          tf.data.Dataset.from_generator(
              __a			,				({k: tf.intaa for k in input_names}, tf.intaa)			,				({k: tf.TensorShape([None]      ) for k in input_names}, tf.TensorShape([]      ))			,				)
          if datasets.Split.TRAIN in transformed_ds
          else None
      )

      if train_ds is not None:
            a__	:  Optional[int]     =   train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]      )      )      )

      a__	:  Union[str, Any]     =   (
          tf.data.Dataset.from_generator(
              __a			,				({k: tf.intaa for k in input_names}, tf.intaa)			,				({k: tf.TensorShape([None]      ) for k in input_names}, tf.TensorShape([]      ))			,				)
          if datasets.Split.VALIDATION in transformed_ds
          else None
      )

      if val_ds is not None:
            a__	:  Optional[Any]     =   val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]      )      )      )

      a__	:  Union[str, Any]     =   (
          tf.data.Dataset.from_generator(
              __a			,				({k: tf.intaa for k in input_names}, tf.intaa)			,				({k: tf.TensorShape([None]      ) for k in input_names}, tf.TensorShape([]      ))			,				)
          if datasets.Split.TEST in transformed_ds
          else None
      )

      if test_ds is not None:
            a__	:  Tuple     =   test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]      )      )      )

      return train_ds, val_ds, test_ds, labelaid


UpperCamelCase					:       Optional[Any]			    =     logging.getLogger(__name__)



@dataclass
class A__     :



  """simple docstring"""



  _lowercase									=		field(metadata={'help': 'Which column contains the label'}  )
  _lowercase									=		field(default=A__				,		metadata={'help': 'The path of the training file'}  )
  _lowercase									=		field(default=A__				,		metadata={'help': 'The path of the development file'}  )
  _lowercase									=		field(default=A__				,		metadata={'help': 'The path of the test file'}  )
  _lowercase									=		field(
      default=1_2_8				,		metadata={
          'help': (
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )
      }				,		)
  _lowercase									=		field(
      default=A__				,		metadata={'help': 'Overwrite the cached training and evaluation sets'}  )




@dataclass
class A__     :



  """simple docstring"""



  _lowercase									=		field(
      metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}  )
  _lowercase									=		field(
      default=A__				,		metadata={'help': 'Pretrained config name or path if not the same as model_name'}  )
  _lowercase									=		field(
      default=A__				,		metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}  )
  _lowercase									=		field(default=A__				,		metadata={'help': 'Set this flag to use fast tokenization.'}  )
  # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
  # or just modify its tokenizer_config.json.
  _lowercase									=		field(
      default=A__				,		metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}				,		)




def UpperCamelCase_				(				)	->   Union[str, Any]:
      # See all possible arguments in src/transformers/training_args.py
      # or by passing the --help flag to this script.
      # We now keep distinct sets of args, for a cleaner separation of concerns.
      a__	:  str     =   HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)      )
      a__,	a__,	a__	:  str     =   parser.parse_args_into_dataclasses()

      if (
          os.path.exists(training_args.output_dir      )
          and os.listdir(training_args.output_dir      )
          and training_args.do_train
          and not training_args.overwrite_output_dir
      ):
            raise ValueError(
                f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
                " --overwrite_output_dir to overcome."      )

      # Setup logging
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"			,				datefmt="%m/%d/%Y %H:%M:%S"			,				level=logging.INFO			,				)
      logger.info(
          f'''n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1      )}, '''
          f'''16-bits training: {training_args.fpaa}'''      )
      logger.info(f'''Training/evaluation parameters {training_args}'''      )

      # Load pretrained model and tokenizer
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.

      a__	:  Union[str, Any]     =   AutoTokenizer.from_pretrained(
          model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path			,				cache_dir=model_args.cache_dir			,				)

      a__,	a__,	a__,	a__	:  Optional[Any]     =   get_tfds(
          train_file=data_args.train_file			,				eval_file=data_args.dev_file			,				test_file=data_args.test_file			,				tokenizer=__a			,				label_column_id=data_args.label_column_id			,				max_seq_length=data_args.max_seq_length			,				)

      a__	:  Optional[int]     =   AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path			,				num_labels=len(__a      )			,				labelaid=__a			,				idalabel={id: label for label, id in labelaid.items()}			,				finetuning_task="text-classification"			,				cache_dir=model_args.cache_dir			,				)

      with training_args.strategy.scope():
            a__	:  Any     =   TFAutoModelForSequenceClassification.from_pretrained(
                model_args.model_name_or_path			,				from_pt=bool(".bin" in model_args.model_name_or_path      )			,				config=__a			,				cache_dir=model_args.cache_dir			,				)

      def compute_metrics(__a      ) -> Dict:
            a__	:  Union[str, Any]     =   np.argmax(p.predictions			,				axis=1      )

            return {"acc": (preds == p.label_ids).mean()}

      # Initialize our Trainer
      a__	:  Dict     =   TFTrainer(
          model=__a			,				args=__a			,				train_dataset=__a			,				eval_dataset=__a			,				compute_metrics=__a			,				)

      # Training
      if training_args.do_train:
            trainer.train()
            trainer.save_model()
            tokenizer.save_pretrained(training_args.output_dir      )

      # Evaluation
      a__	:  Optional[Any]     =   {}
      if training_args.do_eval:
            logger.info("*** Evaluate ***"      )
            a__	:  Dict     =   trainer.evaluate()
            a__	:  int     =   os.path.join(training_args.output_dir			,				"eval_results.txt"      )

            with open(__a			,				"w"      ) as writer:
                  logger.info("***** Eval results *****"      )

                  for key, value in result.items():
                        logger.info(f'''  {key} = {value}'''      )
                        writer.write(f'''{key} = {value}\n'''      )

                  results.update(__a      )

      return results


if __name__ == "__main__":
    main()









'''simple docstring'''







import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation

__lowerCAmelCase        =							logging.get_logger(__name__)


__lowerCAmelCase        =							{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

__lowerCAmelCase        =							{
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

__lowerCAmelCase        =							{"facebook/blenderbot-3B": 1_28}


@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def       __UpperCamelCase					(      ):
       """simple docstring"""




       a_						     =			(
           list(range(ord('!'   )   ,       ord('~'   ) + 1   )   ) + list(range(ord('¡'   )   ,       ord('¬'   ) + 1   )   ) + list(range(ord('®'   )   ,       ord('ÿ'   ) + 1   )   )
       )
       a_						     =			bs[:]
       a_						     =			0
       for b in range(2**8   ):
              if b not in bs:
                     bs.append(lowercase_   )
                     cs.append(2**8 + n   )
                     n += 1
       a_						     =			[chr(lowercase_   ) for n in cs]
       return dict(zip(lowercase_   ,       lowercase_   )   )


def       __UpperCamelCase					(      lowercase_       :  int   ):
       """simple docstring"""




       a_						     =			set()
       a_						     =			word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)   )
              a_						     =			char
       return pairs




class   __SCREAMING_SNAKE_CASE       (__A       ):


  """simple docstring"""


  _a  : Dict      			=     VOCAB_FILES_NAMES
  _a  : Union[str, Any]      			=     PRETRAINED_VOCAB_FILES_MAP
  _a  : Optional[int]      			=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _a  : str      			=     ['''input_ids''', '''attention_mask''']







  def __init__(			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__="replace"   ,	UpperCamelCase__="<s>"   ,	UpperCamelCase__="</s>"   ,	UpperCamelCase__="</s>"   ,	UpperCamelCase__="<s>"   ,	UpperCamelCase__="<unk>"   ,	UpperCamelCase__="<pad>"   ,	UpperCamelCase__="<mask>"   ,	UpperCamelCase__=False   ,	**UpperCamelCase__   ,	):






         """simple docstring"""


         a_						     =			AddedToken(UpperCamelCase__   ,	lstrip=UpperCamelCase__   ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else bos_token
         a_						     =			AddedToken(UpperCamelCase__   ,	lstrip=UpperCamelCase__   ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else eos_token
         a_						     =			AddedToken(UpperCamelCase__   ,	lstrip=UpperCamelCase__   ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else sep_token
         a_						     =			AddedToken(UpperCamelCase__   ,	lstrip=UpperCamelCase__   ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else cls_token
         a_						     =			AddedToken(UpperCamelCase__   ,	lstrip=UpperCamelCase__   ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else unk_token
         a_						     =			AddedToken(UpperCamelCase__   ,	lstrip=UpperCamelCase__   ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else pad_token

         # Mask token behave like a normal word, i.e. include the space before it
         a_						     =			AddedToken(UpperCamelCase__   ,	lstrip=UpperCamelCase__   ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else mask_token

         super().__init__(
             errors=UpperCamelCase__   ,	bos_token=UpperCamelCase__   ,	eos_token=UpperCamelCase__   ,	unk_token=UpperCamelCase__   ,	sep_token=UpperCamelCase__   ,	cls_token=UpperCamelCase__   ,	pad_token=UpperCamelCase__   ,	mask_token=UpperCamelCase__   ,	add_prefix_space=UpperCamelCase__   ,	**UpperCamelCase__   ,	)

         with open(UpperCamelCase__   ,	encoding='utf-8'	) as vocab_handle:
                a_						     =			json.load(UpperCamelCase__	)
         a_						     =			{v: k for k, v in self.encoder.items()}
         a_						     =			errors  # how to handle errors in decoding
         a_						     =			bytes_to_unicode()
         a_						     =			{v: k for k, v in self.byte_encoder.items()}
         with open(UpperCamelCase__   ,	encoding='utf-8'	) as merges_handle:
                a_						     =			merges_handle.read().split('\n'	)[1:-1]
         a_						     =			[tuple(merge.split()	) for merge in bpe_merges]
         a_						     =			dict(zip(UpperCamelCase__   ,	range(len(UpperCamelCase__	)	)	)	)
         a_						     =			{}
         a_						     =			add_prefix_space

         # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
         a_						     =			re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'	)







  @property
  # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
  def    _a       (			self	):






         """simple docstring"""


         return len(self.encoder	)







  def    _a       (			self	):






         """simple docstring"""


         return dict(self.encoder   ,	**self.added_tokens_encoder	)







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         if token in self.cache:
                return self.cache[token]
         a_						     =			tuple(UpperCamelCase__	)
         a_						     =			get_pairs(UpperCamelCase__	)

         if not pairs:
                return token

         while True:
                a_						     =			min(UpperCamelCase__   ,	key=lambda UpperCamelCase__	: self.bpe_ranks.get(UpperCamelCase__   ,	float('inf'	)	)	)
                if bigram not in self.bpe_ranks:
                       break
                a_     ,	a_						     =			bigram
                a_						     =			[]
                a_						     =			0
                while i < len(UpperCamelCase__	):
                       try:
                              a_						     =			word.index(UpperCamelCase__   ,	UpperCamelCase__	)
                       except ValueError:
                              new_word.extend(word[i:]	)
                              break
                       else:
                              new_word.extend(word[i:j]	)
                              a_						     =			j

                       if word[i] == first and i < len(UpperCamelCase__	) - 1 and word[i + 1] == second:
                              new_word.append(first + second	)
                              i += 2
                       else:
                              new_word.append(word[i]	)
                              i += 1
                a_						     =			tuple(UpperCamelCase__	)
                a_						     =			new_word
                if len(UpperCamelCase__	) == 1:
                       break
                else:
                       a_						     =			get_pairs(UpperCamelCase__	)
         a_						     =			' '.join(UpperCamelCase__	)
         a_						     =			word
         return word







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			[]
         for token in re.findall(self.pat   ,	UpperCamelCase__	):
                a_						     =			''.join(
                    self.byte_encoder[b] for b in token.encode('utf-8'	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
                bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCamelCase__	).split(' '	)	)
         return bpe_tokens







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         return self.encoder.get(UpperCamelCase__   ,	self.encoder.get(self.unk_token	)	)







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         return self.decoder.get(UpperCamelCase__	)







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			''.join(UpperCamelCase__	)
         a_						     =			bytearray([self.byte_decoder[c] for c in text]	).decode('utf-8'   ,	errors=self.errors	)
         return text







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__ = None	):






         """simple docstring"""


         if not os.path.isdir(UpperCamelCase__	):
                logger.error(f'Vocabulary path ({save_directory}) should be a directory'	)
                return
         a_						     =			os.path.join(
             UpperCamelCase__   ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)
         a_						     =			os.path.join(
             UpperCamelCase__   ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']	)

         with open(UpperCamelCase__   ,	'w'   ,	encoding='utf-8'	) as f:
                f.write(json.dumps(self.encoder   ,	indent=2   ,	sort_keys=UpperCamelCase__   ,	ensure_ascii=UpperCamelCase__	) + '\n'	)

         a_						     =			0
         with open(UpperCamelCase__   ,	'w'   ,	encoding='utf-8'	) as writer:
                writer.write('#version: 0.2\n'	)
                for bpe_tokens, token_index in sorted(self.bpe_ranks.items()   ,	key=lambda UpperCamelCase__	: kv[1]	):
                       if index != token_index:
                              logger.warning(
                                  f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                                  ' Please check that the tokenizer is not corrupted!'	)
                              a_						     =			token_index
                       writer.write(' '.join(UpperCamelCase__	) + '\n'	)
                       index += 1

         return vocab_file, merge_file







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = False	):






         """simple docstring"""


         if already_has_special_tokens:
                return super().get_special_tokens_mask(
                    token_ids_a=UpperCamelCase__   ,	token_ids_a=UpperCamelCase__   ,	already_has_special_tokens=UpperCamelCase__	)

         if token_ids_a is None:
                return [1] + ([0] * len(UpperCamelCase__	)) + [1]
         return [1] + ([0] * len(UpperCamelCase__	)) + [1, 1] + ([0] * len(UpperCamelCase__	)) + [1]







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__ = None	):






         """simple docstring"""


         a_						     =			[self.sep_token_id]
         a_						     =			[self.cls_token_id]

         if token_ids_a is None:
                return len(cls + token_ids_a + sep	) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__=False   ,	**UpperCamelCase__	):






         """simple docstring"""


         a_						     =			kwargs.pop('add_prefix_space'   ,	self.add_prefix_space	)
         if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase__	) > 0 and not text[0].isspace()):
                a_						     =			' ' + text
         return (text, kwargs)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__ = None	):






         """simple docstring"""


         return token_ids_a + [self.eos_token_id]







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			[]
         for is_user, text in conversation.iter_texts():
                if is_user:
                       # We need to space prefix as it's being done within blenderbot
                       inputs.append(' ' + text	)
                else:
                       # Generated responses should contain them already.
                       inputs.append(UpperCamelCase__	)

         a_						     =			'  '.join(UpperCamelCase__	)
         a_						     =			self.encode(UpperCamelCase__	)
         if len(UpperCamelCase__	) > self.model_max_length:
                a_						     =			input_ids[-self.model_max_length :]
                logger.warning(f'Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'	)
         return input_ids




